Application of multivariate techniques for estimating herd feed efficiency using chemical and near-infrared
Valentina Novara1, Mattia Masseroni1,2, Maddalena Canossa1
1Department of Animal Science, Food and Nutrition (DIANA), Università Cattolica Sacro Cuore, 29122 Piacenza, Italy.
Abstract:
Feed efficiency (FE) is an indicator of overall farm nutritional efficiency, helping farmers to identify any critical points in nutritional management. Particularly, FE is a measure of the ability of animals to convert feed into milk and it can be influenced by genetic, health, management, and nutritional factors. Higher FE allows results in reduced feed and maintenance costs and contributes to improved economic and environmental efficiency of dairy farms. This study aimed to develop and compare 2 predictive models for estimating FE in dairy cattle using data derived from the TMR: one based on its chemical composition and the other on near-infrared (NIR) spectral data. A total of 144 TMR samples were collected from farms in Po Valley from 2021 to 2024 and analyzed with an Fourier-transform NIR spectrometer. The spectral data were processed with chemometric techniques, including least absolute shrinkage and selection operator regression, in order to build a predictive model of FE. The model based on chemical composition showed strong calibration performance (R2 = 0.80, SE of cross-validation [SECV] = 0.13) but decreased in external validation (R2 = 0.64, SE of prediction [SEP] = 0.11), indicating the presence of systematic bias. Conversely, the NIR-based model maintained more stable performance between calibration (R2 = 0.73, SECV = 0.16) and external validation (R2 = 0.70, SEP = 0.09), with lower slope distortion and offset. The results suggest that although chemical data offer high accuracy in controlled conditions, the NIR model may be more robust and generalizable for practical, on-farm prediction of FE, offering potential decision support. However, further improvements in calibration are needed to reduce systematic errors and increase the accuracy of the model.
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